ARIMA simulato

Prima di analizzare serie storiche reali, prova a lavorare con un modello un po' più complesso.

Qui abbiamo generato 250 osservazioni dal modello ARIMA(2,1,0) con drift, dato da $$Y_t = 1 + 1.5 Y_{t-1} - .75 Y_{t-2} + W_t\,$$ dove \(Y_t = \nabla X_t = X_{t} - X_{t-1}\).

Userai le tecniche già viste per adattare un modello ai dati.

Il pacchetto astsa è precaricato e i dati generati sono in x. La serie x e la serie detrendizzata y <- diff(x) sono già state tracciate.

Questo esercizio fa parte del corso

Modelli ARIMA in R

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Istruzioni dell'esercizio

Traccia l'ACF e la PACF campionarie usando acf2() sui dati differenziati diff(x) per determinare un modello.
Adatta un modello ARIMA(2,1,0) ai dati generati usando sarima(). Esamina la t-table e le altre informazioni dell'output per valutare l'adattamento del modello.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Plot sample P/ACF of differenced data and determine model



# Estimate parameters and examine output

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Questo esercizio fa parte del corso

Modelli ARIMA in R

InicianteNível de habilidade

4.9+

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You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

Exercise 1: First things first Exercise 2: Data play Exercise 3: Elements of time series Exercise 4: Stationarity and nonstationarity Exercise 5: Differencing Exercise 6: Detrending data Exercise 7: Dealing with trend and heteroscedasticity Exercise 8: Stationary time series: ARMA Exercise 9: Simulating ARMA models

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.

Exercise 1: AR and MA models Exercise 2: Fitting an AR(1) model Exercise 3: Fitting an AR(2) model Exercise 4: Fitting an MA(1) model Exercise 5: AR and MA together Exercise 6: Fitting an ARMA model Exercise 7: Identify an ARMA model Exercise 8: Model choice and residual analysis Exercise 9: Model choice - I Exercise 10: Model choice - II Exercise 11: Residual analysis - I Exercise 12: Residual analysis - II Exercise 13: ARMA get in

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.

Exercise 1: ARIMA - ARMA integrato Exercise 2: ARIMA - plug and play Exercise 3: ARIMA simulato

Esercizio in corso

Exercise 4: Riscaldamento globale Exercise 5: Diagnostica ARIMA Exercise 6: Diagnostica - overfitting simulato Exercise 7: Diagnostica - temperature globali Exercise 8: Previsioni con ARIMA Exercise 9: Previsioni con ARIMA simulato Exercise 10: Previsione delle temperature globali

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

Exercise 1: Pure seasonal models Exercise 2: P/ACF of pure seasonal models Exercise 3: Fit a pure seasonal model Exercise 4: Mixed seasonal models Exercise 5: Fit a mixed seasonal model Exercise 6: Data analysis - unemployment I Exercise 7: Data analysis - unemployment II Exercise 8: Data analysis - commodity prices Exercise 9: Data analysis - birth rate Exercise 10: Forecasting seasonal ARIMA Exercise 11: Forecasting monthly unemployment Exercise 12: How hard is it to forecast commodity prices?Exercise 13: Congratulations!